267 citations · 399 across the 9 of their papers we have counts for
4 papers · 1 filter
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to r…
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage +10
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for lit…
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage +9
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distor…
Robust and Communication-Efficient Federated Learning from Non-IID Data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller +1
Federated Learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a cen…